期刊
PATTERN RECOGNITION
卷 45, 期 5, 页码 1927-1936出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2011.11.010
关键词
Object detection; Shape similarity; Dominant sets
资金
- DOE [71498-001-09]
- AFOSR [FA9550-09-1-0207]
- NSF [IIS-0812118, BCS-0924164, OIA-1027897]
- Direct For Social, Behav & Economic Scie
- Division Of Behavioral and Cognitive Sci [924164] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [812118] Funding Source: National Science Foundation
- Office of Integrative Activities
- Office Of The Director [1027897] Funding Source: National Science Foundation
Contour-based object detection can be formulated as a matching problem between model contour parts and image edge fragments. We propose a novel solution by treating this problem as the problem of finding dominant sets in weighted graphs. The nodes of the graph are pairs composed of model contour parts and image edge fragments, and the weights between nodes are based on shape similarity. Because of high consistency between correct correspondences, the correct matching corresponds to a dominant set of the graph. Consequently, when a dominant set is determined, it provides a selection of correct correspondences. As the proposed method is able to get all the dominant sets, we can detect multiple objects in an image in one pass. Moreover, since our approach is purely based on shape, we also determine an optimal scale of target object without a common enumeration of all possible scales. Both theoretic analysis and extensive experimental evaluation illustrate the benefits of our approach. (C) 2011 Elsevier Ltd. All rights reserved.
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